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Full-Text Articles in Computer Sciences
Automatically Classifying Non-Functional Requirements With Feature Extraction And Supervised Machine Learning Techniques, Mahtab Ezzatikarami
Automatically Classifying Non-Functional Requirements With Feature Extraction And Supervised Machine Learning Techniques, Mahtab Ezzatikarami
Electronic Thesis and Dissertation Repository
Abstract. Context and Motivation: Non-functional requirements (NFRs) of a system need to be classified into different types such as usability, performance, etc. This would enable stakeholders to ensure the completeness of their work by extracting specific NFRs related to their expertise. Question/Problem: Because of the size and complexity of requirement specification documents, the manual classification of NFRs is time-consuming, labour-intensive, and error-prone. We thus need an automated solution that can provide a highly accurate and efficient categorization of NFRs. Principal ideas/results: In this investigation, using natural language processing and supervised machine learning (SML) techniques, we investigate with feature extraction techniques …
An Adaptive Weighted Average (Wav) Reprojection Algorithm For Image Denoising, Halimah Alsurayhi
An Adaptive Weighted Average (Wav) Reprojection Algorithm For Image Denoising, Halimah Alsurayhi
Electronic Thesis and Dissertation Repository
Patch-based denoising algorithms have an effective improvement in the image denoising domain. The Non-Local Means (NLM) algorithm is the most popular patch-based spatial domain denoising algorithm. Many variants of the NLM algorithm have proposed to improve its performance. Weighted Average (WAV) reprojection algorithm is one of the most effective improvements of the NLM denoising algorithm. Contrary to the NLM algorithm, all the pixels in the patch contribute into the averaging process in the WAV reprojection algorithm, which enhances the denoising performance. The key parameters in the WAV reprojection algorithm are kept fixed regardless of the image structure. In this thesis, …
Baseline Assisted Classification Of Heart Rate Variability, Elham Harirpoush
Baseline Assisted Classification Of Heart Rate Variability, Elham Harirpoush
Electronic Thesis and Dissertation Repository
Recently, among various analysis methods of physiological signals, automatic analysis of Electrocardiogram (ECG) signals, especially heart rate variability (HRV) has received significant attention in the field of machine learning. Heart rate variability is an important indicator of health prediction and it is applicable to various fields of scientific research. Heart rate variability is based on measuring the differences in time between consecutive heartbeats (also known as RR interval), and the most common measuring techniques are divided into the time domain and frequency domain. In this research study, a classifier based on analysis of HRV signal is developed to classify different …
Bm3d Image Denoising Using Learning-Based Adaptive Hard Thresholding, Farhan Bashar
Bm3d Image Denoising Using Learning-Based Adaptive Hard Thresholding, Farhan Bashar
Electronic Thesis and Dissertation Repository
Image denoising is an important pre-processing step in most imaging applications. Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard thresholding scheme to attenuate noise from a 3D block. Experiments show that this fixed hard thresholding deteriorates the performance of BM3D because it does not consider the context of corresponding blocks. In this thesis, we propose a learning based adaptive hard thresholding method to solve this issue. Also, BM3D algorithm requires as an input the value of the noise level in the input image. …